#! /bin/bash

DIR_PREFIX=$WORK_DIR
REL_DIR_NAME={}
TRAIN_TYPE=train_ds
DEV_TYPE=dev_diag
TEST_TYPE=test_human
LOG_NAME=log.agent.$REL_DIR_NAME

arg_random_seed=0
# data, model parameters
arg_train_file=$DIR_PREFIX/data/$REL_DIR_NAME/$TRAIN_TYPE.csv
arg_dev_file=$DIR_PREFIX/data/$REL_DIR_NAME/$DEV_TYPE.csv
arg_test_file=$DIR_PREFIX/data/$REL_DIR_NAME/$TEST_TYPE.csv
arg_vocab_freq_file=$DIR_PREFIX/data/$REL_DIR_NAME/vocab_freq.csv
arg_model_dir=$DIR_PREFIX/model/$REL_DIR_NAME
arg_model_store_name_prefix=att_bi_lstm.train_ds
arg_model_resume_name=${arg_model_store_name_prefix}.best
arg_model_resume_suffix=best

# agent parameters
arg_policy_train_filter_prob=0.5
arg_policy_reward_eta=1.0
arg_policy_reward_gamma=1.0
arg_print_reward_freq=1000
arg_policy_max_epoch=10
arg_policy_batch_size=5
arg_policy_sample_cnt=5
arg_policy_eps=0.1
arg_policy_eps_decay=0.9

# other parameters
arg_model_type=AttBiLSTM
arg_train_label_type=soft
arg_print_loss_freq=1000
arg_max_epoch=5
arg_fix_seq_len=-1
arg_min_word_freq=3
arg_word_vec_size=100
arg_pretrained_word_vectors=glove.6B.100d
arg_pos_max_len=60
arg_pos_vec_size=5
arg_class_size=2
arg_hidden_size=200
arg_emb_dropout_p=0.3
arg_lstm_dropout_p=0.3
arg_last_dropout_p=0.5
arg_train_batch_size=50
arg_test_batch_size=500
arg_optimizer_type=Adam
arg_optimize_parameters=all
arg_mask_init_weight=None
arg_learning_rate=1e-3
arg_momentum=0.5
arg_weight_decay=0.0
arg_lr_truncate=5e-2
arg_gravity=1e-2
arg_truncate_freq=1
